Real-time prediction of the rock mass class in front of the tunnel face is essential for the adaptive adjustment of tunnel boring machines(TBMs).During the TBM tunnelling process,a large number of operation data are g...Real-time prediction of the rock mass class in front of the tunnel face is essential for the adaptive adjustment of tunnel boring machines(TBMs).During the TBM tunnelling process,a large number of operation data are generated,reflecting the interaction between the TBM system and surrounding rock,and these data can be used to evaluate the rock mass quality.This study proposed a stacking ensemble classifier for the real-time prediction of the rock mass classification using TBM operation data.Based on the Songhua River water conveyance project,a total of 7538 TBM tunnelling cycles and the corresponding rock mass classes are obtained after data preprocessing.Then,through the tree-based feature selection method,10 key TBM operation parameters are selected,and the mean values of the 10 selected features in the stable phase after removing outliers are calculated as the inputs of classifiers.The preprocessed data are randomly divided into the training set(90%)and test set(10%)using simple random sampling.Besides stacking ensemble classifier,seven individual classifiers are established as the comparison.These classifiers include support vector machine(SVM),k-nearest neighbors(KNN),random forest(RF),gradient boosting decision tree(GBDT),decision tree(DT),logistic regression(LR)and multilayer perceptron(MLP),where the hyper-parameters of each classifier are optimised using the grid search method.The prediction results show that the stacking ensemble classifier has a better performance than individual classifiers,and it shows a more powerful learning and generalisation ability for small and imbalanced samples.Additionally,a relative balance training set is obtained by the synthetic minority oversampling technique(SMOTE),and the influence of sample imbalance on the prediction performance is discussed.展开更多
随着全氟和多氟化合物(perfluoroalkyl and polyfluoroalkyl substances,PFASs)被列入《斯德哥尔摩公约》的持久性有机污染物名录,各国对于该类物质的关注逐步升高。该类物质在环境中的广泛检出,使得其环境行为研究不断扩展和加深。目前...随着全氟和多氟化合物(perfluoroalkyl and polyfluoroalkyl substances,PFASs)被列入《斯德哥尔摩公约》的持久性有机污染物名录,各国对于该类物质的关注逐步升高。该类物质在环境中的广泛检出,使得其环境行为研究不断扩展和加深。目前,针对不同类型PFASs的样品前处理方式与检测方法也在不断发展中,而从中选择最合适的前处理和分析方法是开展PFASs环境科学、管理和污染控制研究的前提。该文针对传统PFASs及其异构体、PFASs前体物和新型PFASs等的样品前处理方法、色谱-质谱分析方法进行归纳总结,认识其现状和问题,并在此基础上对其发展进行了展望。展开更多
Using ultra-fine sample for determination 42 elements by pressurized acid digestion -ICP-MS, the mass of test portion can be reduced to 2 mg yet maintain the representation. And acid used for digestion could be reduce...Using ultra-fine sample for determination 42 elements by pressurized acid digestion -ICP-MS, the mass of test portion can be reduced to 2 mg yet maintain the representation. And acid used for digestion could be reduced to less than 0.5 mL, reaction time also largely reduced.展开更多
The scientific community has shown great interest in the field of mass spectrometry-based proteomics and peptidomics for its applications in biology. Proteomics technologies have evolved to produce large data sets of ...The scientific community has shown great interest in the field of mass spectrometry-based proteomics and peptidomics for its applications in biology. Proteomics technologies have evolved to produce large data sets of proteins or peptides involved in various biologic and disease progression processes generating testable hypothesis for complex biologic questions. This review provides an introduction to relevant topics in proteomics and peptidomics including biologic material selection, sample preparation, separation techniques, peptide fragmentation, post-translational modifications, quantification, bioinformatics, and biomarker discovery and validation. In addition, current literature, remaining challenges, and emerging technologies for proteomics and peptidomics are presented.展开更多
Breast mass identification is of great significance for early screening of breast cancer,while the existing detection methods have high missed and misdiagnosis rate for small masses.We propose a small target breast ma...Breast mass identification is of great significance for early screening of breast cancer,while the existing detection methods have high missed and misdiagnosis rate for small masses.We propose a small target breast mass detection network named Residual asymmetric dilated convolution-Cross layer attention-Mean standard deviation adaptive selection-You Only Look Once(RCM-YOLO),which improves the identifiability of small masses by increasing the resolution of feature maps,adopts residual asymmetric dilated convolution to expand the receptive field and optimize the amount of parameters,and proposes the cross-layer attention that transfers the deep semantic information to the shallow layer as auxiliary information to obtain key feature locations.In the training process,we propose an adaptive positive sample selection algorithm to automatically select positive samples,which considers the statistical features of the intersection over union sets to ensure the validity of the training set and the detection accuracy of the model.To verify the performance of our model,we used public datasets to carry out the experiments.The results showed that the mean Average Precision(mAP)of RCM-YOLO reached 90.34%,compared with YOLOv5,the missed detection rate for small masses of RCM-YOLO was reduced to 11%,and the single detection time was reduced to 28 ms.The detection accuracy and speed can be effectively improved by strengthening the feature expression of small masses and the relationship between features.Our method can help doctors in batch screening of breast images,and significantly promote the detection rate of small masses and reduce misdiagnosis.展开更多
基金funded by the National Natural Science Foundation of China(Grant No.41941019)the State Key Laboratory of Hydroscience and Engineering(Grant No.2019-KY-03)。
文摘Real-time prediction of the rock mass class in front of the tunnel face is essential for the adaptive adjustment of tunnel boring machines(TBMs).During the TBM tunnelling process,a large number of operation data are generated,reflecting the interaction between the TBM system and surrounding rock,and these data can be used to evaluate the rock mass quality.This study proposed a stacking ensemble classifier for the real-time prediction of the rock mass classification using TBM operation data.Based on the Songhua River water conveyance project,a total of 7538 TBM tunnelling cycles and the corresponding rock mass classes are obtained after data preprocessing.Then,through the tree-based feature selection method,10 key TBM operation parameters are selected,and the mean values of the 10 selected features in the stable phase after removing outliers are calculated as the inputs of classifiers.The preprocessed data are randomly divided into the training set(90%)and test set(10%)using simple random sampling.Besides stacking ensemble classifier,seven individual classifiers are established as the comparison.These classifiers include support vector machine(SVM),k-nearest neighbors(KNN),random forest(RF),gradient boosting decision tree(GBDT),decision tree(DT),logistic regression(LR)and multilayer perceptron(MLP),where the hyper-parameters of each classifier are optimised using the grid search method.The prediction results show that the stacking ensemble classifier has a better performance than individual classifiers,and it shows a more powerful learning and generalisation ability for small and imbalanced samples.Additionally,a relative balance training set is obtained by the synthetic minority oversampling technique(SMOTE),and the influence of sample imbalance on the prediction performance is discussed.
文摘随着全氟和多氟化合物(perfluoroalkyl and polyfluoroalkyl substances,PFASs)被列入《斯德哥尔摩公约》的持久性有机污染物名录,各国对于该类物质的关注逐步升高。该类物质在环境中的广泛检出,使得其环境行为研究不断扩展和加深。目前,针对不同类型PFASs的样品前处理方式与检测方法也在不断发展中,而从中选择最合适的前处理和分析方法是开展PFASs环境科学、管理和污染控制研究的前提。该文针对传统PFASs及其异构体、PFASs前体物和新型PFASs等的样品前处理方法、色谱-质谱分析方法进行归纳总结,认识其现状和问题,并在此基础上对其发展进行了展望。
文摘Using ultra-fine sample for determination 42 elements by pressurized acid digestion -ICP-MS, the mass of test portion can be reduced to 2 mg yet maintain the representation. And acid used for digestion could be reduced to less than 0.5 mL, reaction time also largely reduced.
文摘The scientific community has shown great interest in the field of mass spectrometry-based proteomics and peptidomics for its applications in biology. Proteomics technologies have evolved to produce large data sets of proteins or peptides involved in various biologic and disease progression processes generating testable hypothesis for complex biologic questions. This review provides an introduction to relevant topics in proteomics and peptidomics including biologic material selection, sample preparation, separation techniques, peptide fragmentation, post-translational modifications, quantification, bioinformatics, and biomarker discovery and validation. In addition, current literature, remaining challenges, and emerging technologies for proteomics and peptidomics are presented.
基金supported by the National Natural Science Foundation of China(No.62271264)the National Key Research and Development Program of China(No.2021ZD0102100)the Industry University Research Foundation of Jiangsu Province(No.BY2022459).
文摘Breast mass identification is of great significance for early screening of breast cancer,while the existing detection methods have high missed and misdiagnosis rate for small masses.We propose a small target breast mass detection network named Residual asymmetric dilated convolution-Cross layer attention-Mean standard deviation adaptive selection-You Only Look Once(RCM-YOLO),which improves the identifiability of small masses by increasing the resolution of feature maps,adopts residual asymmetric dilated convolution to expand the receptive field and optimize the amount of parameters,and proposes the cross-layer attention that transfers the deep semantic information to the shallow layer as auxiliary information to obtain key feature locations.In the training process,we propose an adaptive positive sample selection algorithm to automatically select positive samples,which considers the statistical features of the intersection over union sets to ensure the validity of the training set and the detection accuracy of the model.To verify the performance of our model,we used public datasets to carry out the experiments.The results showed that the mean Average Precision(mAP)of RCM-YOLO reached 90.34%,compared with YOLOv5,the missed detection rate for small masses of RCM-YOLO was reduced to 11%,and the single detection time was reduced to 28 ms.The detection accuracy and speed can be effectively improved by strengthening the feature expression of small masses and the relationship between features.Our method can help doctors in batch screening of breast images,and significantly promote the detection rate of small masses and reduce misdiagnosis.
